The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I- V and P- V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported. © 2012 Elsevier Ltd.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
Bonanno, F., Capizzi, G., Graditi, G., Napoli, C., & Tina, G. M. (2012). A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Applied Energy, 97, 956 - 961. https://doi.org/10.1016/j.apenergy.2011.12.085